Picture/graphics classification system and method

a classification system and graphic technology, applied in the field of image processing, can solve the problems of affecting the accuracy of image classification, so as to achieve less error, predict a confidence level, and reduce the effect of error

a classification system and graphic technology, applied in the field of image processing, can solve the problems of affecting the accuracy of image classification, so as to achieve less error, predict a confidence level, and reduce the effect of error

US6983068B2Inactive Publication Date: 2006-01-03XEROX CORP
20 Cites 34 Cited by

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Picture/graphics classification system and method
  • Picture/graphics classification system and method
  • Picture/graphics classification system and method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0034]Spatial gray-level dependence (SGLD) techniques for image analysis are well known. SGLD feature extraction creates a two-dimensional histogram that measures first and second-order statistics of an image. These features are captured in SGLD matrices. This was originally proposed for texture analysis of multi-level images. Additionally, since texture features distinguish natural pictures from synthetic graphics, SGLD techniques can be applied to picture / graphics classification of images. A picture / graphics classifier can be created with algorithms that analyze the texture features captured in SGLD matrices. Using the SGLD texture features, the classifier works to determine whether a scanned image is a natural picture or synthetic graphics. Furthermore, in color images, the luminance component typically contains enough information to determine the origin of the image. Therefore, an SGLD matrix that captures the luminance component of an image and a picture / graphics classifier usi...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

A method and system for image processing, in conjunction with classification of images between natural pictures and synthetic graphics, using SGLD texture (e.g., variance, bias, skewness, and fitness), color discreteness (e.g., R—L, R—U, and R—V normalized histograms), or edge features (e.g., pixels per detected edge, horizontal edges, and vertical edges) is provided. In another embodiment, a picture/graphics classifier using combinations of SGLD texture, color discreteness, and edge features is provided. In still another embodiment, a “soft” image classifier using combinations of two (2) or more SGLD texture, color discreteness, and edge features is provided. The “soft” classifier uses image features to classify areas of an input image in picture, graphics, or fuzzy classes.

Description

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
03 Jan 2006
Publication
US6983068B2